Biblio

Vehicular ad hoc networks (VANETs) have become a promising technology in intelligent transportation systems (ITS) with rising interest of expedient, safe, and high-efficient transportation. VANETs are vulnerable to malicious nodes and result in performance degradation because of dynamicity and infrastructure-less. In this paper, we propose a trust based dueling deep reinforcement learning approach (T-DDRL) for communication of connected vehicles, we deploy a dueling network architecture into a logically centralized controller of software-defined networking (SDN). Specifically, the SDN controller is used as an agent to learn the most trusted routing path by deep neural network (DNN) in VANETs, where the trust model is designed to evaluate neighbors' behaviour of forwarding routing information. Simulation results are presented to show the effectiveness of the proposed T-DDRL framework.

With the rising interest of expedient, safe, and high-efficient transportation, vehicular ad hoc networks (VANETs) have turned into a critical technology in smart transportation systems. Because of the high mobility of nodes, VANETs are vulnerable to security attacks. In this paper, we propose a novel framework of software-defined VANETs with trust management. Specifically, we separate the forwarding plane in VANETs from the control plane, which is responsible for the control functionality, such as routing protocols and trust management in VANETs. Using the on-demand distance vector routing (TAODV) protocol as an example, we present a routing protocol named software-defined trust based ad hoc on-demand distance vector routing (SD-TAODV). Simulation results are presented to show the effectiveness of the proposed software-defined VANETs with trust management.